Advanced LTE Relay Network Architecture for Backhauling Mobile Rural Telephony Sites
DOI:
https://doi.org/10.47941/ijce.3223Keywords:
Intelligent Reflecting Surface (IRS), Quantum Machine Learning (QML), Digital Twin, LTE Relay Backhaul, Adaptive Resource AllocationAbstract
This study presents a unique LTE relay backhaul architecture that combines cutting-edge communication and computational intelligence technologies to greatly improve rural telephony networks. The proposed system integrates Intelligent Reflecting Surfaces (IRS) for dynamic SNR enhancement, Quantum Machine Learning (QML) for adaptive beamforming, Digital Twins for predictive maintenance, Neuromorphic Computing for ultra-low latency processing, Blockchain-based resource management, Terahertz (THz) weather adaptation, Graph Neural Networks (GNN) for intelligent routing, and Federated Learning for privacy-preserving analytics. These synergistic technologies address crucial issues such as capacity degradation, latency, interference, environmental unpredictability, and operational resilience. The system dynamically optimizes connection quality using adaptive IRS phase control, while QML algorithms improve spectrum efficiency, and Digital Twins enable real-time health monitoring and proactive maintenance. Extensive simulations and field data show that this integrated architecture outperforms traditional systems in terms of capacity, latency, and BER. The suggested framework provides a scalable, resilient, and forward-looking solution for rural connectivity, paving the path for next-generation 6G networks.
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